**5. The role of whole genome sequencing in the surveillance of antimicrobial resistance of common zoonotic bacteria**

The EU harmonised system on the monitoring and reporting of antimicrobial resistance in zoonotic and commensal bacteria (EC Decision 2013/652/EU; EFSA, EDSA) [12] is based on the phenotypic assessment of AMR of selected bacterial species (*Salmonella, Campylobacter, E. coli*) in selected food-producing animal species (poultry, pigs, cattle) and food products (chicken, pork, beef meat), using dilution methods (ISO standard 20776-1:2006, ISO standard 20776-2:2007) and EUCAST epidemiological cut-off values (ECOFF-values) as interpretative criteria (EC Decision 2013/652/EU) [12]. In accordance with this legislation, 170 isolates are examined for antimicrobial susceptibility to a panel of 15 antimicrobial substances, for each combination of bacterial species and type of sample of animal population or food category each year by each member state. In 2016, ECDC also published EU protocol for harmonised monitoring of antimicrobial resistance in human *Salmonella* and *Campylobacter* isolates, aimed to increase the comparability of AMR data collected at the EU level from different Member States, and to improve the comparison of data among human isolates and isolates from animals and food products [13]. Beside dilution method as a preferred method, disk diffusion and gradient strip diffusion method are also allowed.

On the isolate level, genotyping of human isolates is also recommended, in terms of the assessment of resistance mechanisms and detection of the epidemic spread of resistance, particularly multi-drug resistant *Salmonella,* but it is not required in reporting [13].

In recent years, the development of high-throughput technologies and platforms for massive DNA sequencing, and genomics tools has opened new possibilities also in the surveillance of AMR in common zoonotic bacteria. WGS, together with appropriate databases, general (NCBI, ENA) or specialised for AMR (ARG-ANNOT, ResFinder, CARD, RED-DB, Bacmet), bioinformatic tools (BLAST) and platforms enable detection of antibiotic resistance genetic loci in the genomes of bacterial isolates or microbiomes and reveal the mechanisms leading to AMR. While WGS offers very rapid and efficient tool for detection of the antibiotic resistance genes (ARG) in genomes of individual bacterial isolates, the main issue remains how to predict from these data the actual antimicrobial susceptibility, and epidemiological or clinical cut-off values [85]. However, differentiation among isolates with acquired or intrinsic resistance on the basis of phenotypic MIC determinations only is also not totally accurate. Furthermore, it should be considered that also the strains that contain the genes associated with antimicrobial resistance but do not exhibit phenotypic resistance present certain risk for the horizontal spread when consumed.

of *Campylobacter* from chicken [75]. In humans, the highest rates of FQ resistance were reported for *C. coli* from Italy and Portugal (100%) and in for *C. jejuni* from Portugal and Estonia (>90%).

Overall, 9.2% of human *C. coli* exhibited combined resistance to ciprofloxacin, erythromycin and tetracycline with resistance rates ranging from 0 to 57.9% (Estonia), which is shown in **Figure 7** [5]*.* Erythromycin resistance is often associated with MDR phenotype [63]. In Finland, for example, 94.7% of *Campylobacter* isolates from humans were, in addition to erythromycin, resistant to FQ, and 73.7% to tetracycline [76]. Combined resistance to the first-line drugs may be associated with adverse events such as delayed recovery, invasive illness and

FQ resistance in *C. jejuni* and *C. coli* can be mediated through specific point mutations in *gyrA* gene, encoding for DNA gyrase or through chromosomally encoded multidrug efflux pump. The two mechanisms work synergistically [79]. Efflux pumps in *Campylobacter,* primarily CmeABC, are involved in resistance to broad spectrum of antimicrobials, including macrolides and quinolones [80], as well as cross-resistance to other compounds such as bile salts [81]. Therapeutic application of efflux pump inhibitors (e.g., epigallocatechin gallate) that were shown to restore macrolide efficacy could be a feasible treatment option in combina-

The EU harmonised system on the monitoring and reporting of antimicrobial resistance in zoonotic and commensal bacteria (EC Decision 2013/652/EU; EFSA, EDSA) [12] is based on the phenotypic assessment of AMR of selected bacterial species (*Salmonella, Campylobacter, E. coli*) in selected food-producing animal species (poultry, pigs, cattle) and food products (chicken, pork, beef meat), using dilution methods (ISO standard 20776-1:2006, ISO standard 20776-2:2007) and EUCAST epidemiological cut-off values (ECOFF-values) as interpretative criteria (EC Decision 2013/652/EU) [12]. In accordance with this legislation, 170 isolates are examined for antimicrobial susceptibility to a panel of 15 antimicrobial substances, for each combination of bacterial species and type of sample of animal population or food category each year by each member state. In 2016, ECDC also published EU protocol for harmonised monitoring of antimicrobial resistance in human *Salmonella* and *Campylobacter* isolates, aimed to increase the comparability of AMR data collected at the EU level from different Member States, and to improve the comparison of data among human isolates and isolates from animals and food products [13]. Beside dilution method as a preferred method, disk diffusion

On the isolate level, genotyping of human isolates is also recommended, in terms of the assessment of resistance mechanisms and detection of the epidemic spread of resistance, par-

ticularly multi-drug resistant *Salmonella,* but it is not required in reporting [13].

Notably, 9 out of 19 EU MS recorded 80–100% resistance rates for *C. coli* (**Figure 7**) [5]*.*

prolonged treatment with feasible alternative antimicrobials [77, 78].

**5. The role of whole genome sequencing in the surveillance of** 

**antimicrobial resistance of common zoonotic bacteria**

and gradient strip diffusion method are also allowed.

tion with the macrolide therapy [80, 82–84].

22 Antimicrobial Resistance - A Global Threat

The usefulness of WGS for antimicrobial resistance surveillance was confirmed in several studies. Examination of 640 nontyphoidal *Salmonella* isolates from retail meat and human clinical samples identified known resistance genes and phenotypic resistance to 14 antimicrobials, where the correlation between resistance genotypes and phenotypes was close to 100% for most classes of antibiotics, and lower for aminoglycosides and beta-lactams [86]**.** In addition to known ARG, several unique resistance genes were found, more in the human isolates (n = 59) than in the retail meat isolates (n = 36). The authors concluded that the use of more appropriate MIC breakpoints and inclusion of new AGSs in the databases will further improve the correlations between phenotypic and genotypic observations. For *Salmonella typhimurium* isolates (n = 50) from Danish pigs, high concordance (99.74%) between phenotypic and predicted antimicrobial susceptibility was observed as well [87]. Phenotypic resistance to quinolones and fluoroquinolones due to chromosomal mutations, however, could not be detected by ResFinder platform.

Genomic approach is increasingly used also in the developing of control methods and identification of antimicrobial resistance markers for evidence-based decisions in epidemiology and surveillance of foodborne diseases. OMICS datasets have been found as a powerful tool to complement current studies that are starting to be used also in some risk assessment areas. In a current comprehensive study "Syst-OMICS," 4500 *Salmonella* genomes will be sequenced and analysis pipeline built in order to study *Salmonella* genome evolution, antibiotic resistance and virulence genes [88]. The data of the first 3377 genomes already sequenced are stored in the newly established *Salmonella* Foodborne Syst-OMICS database (SalFoS, https://salfos.ibis. ulaval.ca/). Their analysis identified 1003 unique resistomes, composed of combinations of 195 different genes. Surprisingly, the two most frequently observed resistomes accounted for 23% of the *Salmonella* strains examined.

Comparative genomics of the WGS was successfully used also in the examination of 589 *Campylobacter* isolates from retail chicken meat exhibiting phenotypic resistance to 9 antimicrobials [89]. For most antimicrobial agents (ciprofloxacin, nalidixic acid, gentamicin, azithromycin, erythromycin and clindamycin), the observed phenotypic resistance, determined on the basis of the comparison of measured MICs with established ECOFF cut-off values, was in accordance with the presence of the known resistance genes or mutations. In the case of telithromycin, however, the observed point mutations in the 23S rRNA, which is a well-known mechanism of resistance to these classes of antimicrobials, did not regularly cause phenotypic resistance. Another recent study on C. *jejuni* isolates from the poultry (n = 502) demonstrated successful use of genomics in the study of fluoroquinolone resistance [90]. The isolates were clustered according to the presence/absence of the *gyrA* mutations causing fluoroquinolone resistance. Beside the WGS of isolates from the mentioned study, previously published (ENA) *Campylobacter* genomes were included in the comparative analyses of the genomes. Although no significant associations were found between trade patterns, antimicrobial use in livestock and population of *C. jejuni*, this approach proved to be successful, especially when big datasets are available.

**Author details**

**References**

pdf?ua=1

Vita Rozman, Bojana Bogovič Matijašić and Sonja Smole Možina\*

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Antimicrobial Resistance of Common Zoonotic Bacteria in the Food Chain: An Emerging Threat

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In conclusion, comparative genomics of WGS is increasingly used in the prediction of phenotypic antimicrobial resistance and surveillance of antimicrobial resistance of common zoonotic bacteria. However, as it is based on the detection of already known ARG, the success is highly dependent on the quality of databases, which need to be regularly updated with newly discovered resistance mechanisms and well-curated.
